Multimodal interaction in the perception of impact events displayed via a multichannel audio and simulated structure-borne vibration
Bibliographic record
Abstract
For multimodal display systems in which realistic reproduction of impact events is desired, presenting structure-borne vibration along with multichannel audio recordings has been observed to create a greater sense of immersion in a virtual acoustic environment. Furthermore, there is an increased proportion of reports that the impact event took place within the observer’s local area (this is termed ‘‘presence with’’ the event, in contrast to ‘‘presence in’’ the environment in which the event occurred). While holding the audio reproduction constant, varying the intermodal arrival time and level of mechanically displayed, synthetic whole-body vibration revealed a number of other subjective attributes that depend upon multimodal interaction in the perception of a representative impact event. For example, when the structure-borne component of the displayed impact event arrived 10 to 20 ms later than the airborne component, the intermodal delay was not only tolerated, but gave rise to an increase in the proportion of reports that the impact event had greater power. These results have enabled the refinement of a multimodal simulation in which the manipulation of synthetic whole-body vibration can be used to control perceptual attributes of impact events heard within an acoustic environment reproduced via a multichannel loudspeaker array.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".